Method for predicting symmetric, automated, real-time arc flash energy within a real-time monitoring system

ABSTRACT

A system for making real-time predictions about an arc flash event on an electrical system comprises a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system; an analytics server communicatively connected to the data acquisition component and comprising a virtual system modeling engine configured to generate predicted data output for the electrical system using a virtual system model of the electrical system, an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, and an arc flash simulation engine configured to use the virtual system model updated based in the real-time data to forecast an aspect of the arc flash event.

RELATED APPLICATION INFORMATION

This application claims priority to U.S. application Ser. No.12/506,216, filed Jul. 20, 2009 and entitled “Method for PredictingSymmetric, Automated, Real-Time Arc Flash Energy within a Real-TimeMonitoring System,” which issued on Jul. 23, 2013 as U.S. Pat. No.8,494,830, and which, in turn, claims benefit to U.S. ProvisionalApplication No. 61/082,044, filed Jul. 18, 2008 and entitled “Symmetric,Automated Real-Time Arc Flash,” both of which are hereby incorporatedherein by reference in their entireties as if set forth in full.

This application is also related to U.S. patent application Ser. No.11/771,861, filed Jun. 29, 2007 and entitled “Systems and Methods forProviding A Real-Time Predictions of Arc Flash Incident Energy, ArcFlash Protection Boundary, and Required Personal Protective Equipment(PPE) Levels to Comply with Workplace Safety Standards,” and U.S. patentapplication Ser. No. 12/249,698, filed Oct. 10, 2008 and entitled “AMethod for Predicting Arc Flash Energy and PPE Category within aReal-Time Monitoring System,” both of which are hereby incorporatedherein by reference in their entireties as if set forth in full.

BACKGROUND

I. Technical Field

The present invention relates generally to computer modeling andmanagement of systems and, more particularly, to computer simulationtechniques with real-time system monitoring and prediction of electricalsystem performance.

II. Background

Computer models of complex systems enable improved system design,development, and implementation through techniques for off-linesimulation of the system operation. That is, system models can becreated that computers can “operate” in a virtual environment todetermine design parameters. All manner of systems can be modeled,designed, and virtually operated in this way, including machinery,factories, electrical power and distribution systems, processing plants,devices, chemical processes, biological systems, and the like. Suchsimulation techniques have resulted in reduced development costs andsuperior operation.

Design and production processes have benefited greatly from suchcomputer simulation techniques, and such techniques are relatively welldeveloped, but such techniques have not been applied in real-time, e.g.,for real-time operational monitoring and management. In addition,predictive failure analysis techniques do not generally use real-timedata that reflect actual system operation. Greater efforts at real-timeoperational monitoring and management would provide more accurate andtimely suggestions for operational decisions, and such techniquesapplied to failure analysis would provide improved predictions of systemproblems before they occur. With such improved techniques, operationalcosts could be greatly reduced.

For example, mission critical electrical systems, e.g., for data centersor nuclear power facilities, must be designed to ensure that power isalways available. Thus, the systems must be as failure proof aspossible, and many layers of redundancy must be designed in to ensurethat there is always a backup in case of a failure. It will beunderstood that such systems are highly complex, a complexity made evengreater as a result of the required redundancy. Computer design andmodeling programs allow for the design of such systems by allowing adesigner to model the system and simulate its operation. Thus, thedesigner can ensure that the system will operate as intended before thefacility is constructed.

Once the facility is constructed, however, the design is typically onlyreferred to when there is a failure. In other words, once there isfailure, the system design is used to trace the failure and takecorrective action; however, because such design are complex, and thereare many interdependencies, it can be extremely difficult and timeconsuming to track the failure and all its dependencies and then takecorrective action that does not result in other system disturbances.

Moreover, changing or upgrading the system can similarly be timeconsuming and expensive, requiring an expert to model the potentialchange, e.g., using the design and modeling program. Unfortunately,system interdependencies can be difficult to simulate, making even minorchanges risky.

Conventional static Arc Flash simulation systems use a rigid simulationmodel that does not take the actual power system alignment and agingeffects into consideration when computing predictions about theoperational performance of an electrical system. These systems rely onexhaustive studies to be performed off-line by a power system engineerwho must manually modify a simulation model so that it is reflective ofthe proposed facility operation conditions before conducting the staticsimulation or the series of static simulations. Therefore, they cannotreadily adjust to the many daily changes to the electrical system thatoccur at a facility, e.g., motors and pumps may be put on-line or pulledoff-line, utility electrical feeds may have changed, etc., noraccurately predict the various aspects, i.e., the quantity of energyreleased, the required level of worker PPE, the safe protectionboundaries around components of the electrical system, etc., related toan Arc Flash event occurring on the electrical system.

Moreover, real-time Arc Flash simulations are typically performed bymanually modifying the simulation model of the electrical power systemsuch that the automatic transfer switch (ATS) of the bypass branch ofthe uninterrupted power supply (UPS) component is set to a bypassposition. After, Arc Flash analyses and/or simulations are performedusing the modified simulation model. One challenge with this approach isthat while the Arc Flash analysis and/or simulation is being performed,the simulation model is not identical to the system being modeled. TheArc Flash analysis typically lasts for several seconds. If during thattime another analysis (e.g., power flow, etc.) needs to be performed,the simulation model will not be indicative of the true state of theelectrical power system (as it will have the ATS set to a bypassposition), resulting in misleading data to be generated from theanalyses and/or simulations performed using the modified simulationmodel.

SUMMARY

Methods for making real-time predictions about an Arc Flash event on anelectrical system are disclosed.

In one aspect, a system for making real-time predictions about an arcflash event on an electrical system comprises a data acquisitioncomponent communicatively connected to a sensor configured to acquirereal-time data output from the electrical system; an analytics servercommunicatively connected to the data acquisition component andcomprising a virtual system modeling engine configured to generatepredicted data output for the electrical system using a virtual systemmodel of the electrical system, an analytics engine configured tomonitor the real-time data output and the predicted data output of theelectrical system, and an arc flash simulation engine configured to usethe virtual system model updated based in the real-time data to forecastan aspect of the arc flash event.

These and other features, aspects, and embodiments of the invention aredescribed below in the section entitled “Detailed Description.”

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the principles disclosed herein,and the advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is an illustration of a system for utilizing real-time data forpredictive analysis of the performance of a monitored system, inaccordance with one embodiment;

FIG. 2 is a diagram illustrating a detailed view of an analytics serverincluded in the system of FIG. 1;

FIG. 3 is a diagram illustrating how the system of FIG. 1 operates tosynchronize the operating parameters between a physical facility and avirtual system model of the facility;

FIG. 4 is an illustration of the scalability of a system for utilizingreal-time data for predictive analysis of the performance of a monitoredsystem, in accordance with one embodiment;

FIG. 5 is a block diagram that shows the configuration details of thesystem illustrated in FIG. 1, in accordance with one embodiment;

FIG. 6 is an illustration of a flowchart describing a method forreal-time monitoring and predictive analysis of a monitored system, inaccordance with one embodiment;

FIG. 7 is an illustration of a flowchart describing a method formanaging real-time updates to a virtual system model of a monitoredsystem, in accordance with one embodiment;

FIG. 8 is an illustration of a flowchart describing a method forsynchronizing real-time system data with a virtual system model of amonitored system, in accordance with one embodiment;

FIG. 9 is a flow chart illustrating an example method for updating thevirtual model in accordance with one embodiment;

FIG. 10 is a diagram illustrating an example process for monitoring thestatus of protective devices in a monitored system and updating avirtual model based on monitored data;

FIG. 11 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored;

FIG. 12 is a diagram illustrating an example process for determining theprotective capabilities of a High Voltage Circuit Breaker (HVCB);

FIG. 13 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored inaccordance with another embodiment;

FIG. 14 is a diagram illustrating a process for evaluating the withstandcapabilities of a MVCB in accordance with one embodiment;

FIG. 15 is a diagram illustrating how the Arc Flash Simulation Engineworks in conjunction with the other elements of the analytics system tomake predictions about various aspects of an Arc Flash event on anelectrical system, in accordance with one embodiment;

FIG. 16 is a diagram illustrating an example process for predicting, inreal-time, various aspects associated with an AC or DC Arc Flashincident, in accordance with one embodiment; and

FIG. 17 is a flow chart illustrating an example process for an exampleprocess for predicting, in real-time, various aspects associated with anAC or DC Arc Flash incident using a virtual no load scenario as a secondcritical input, in accordance with one embodiment.

DETAILED DESCRIPTION

Systems and methods for providing real-time predictions of Arch Flashincident energy, Arch Flash protection boundary, and required personalprotective equipment (PPE) to comply with workplace safety standards aredisclosed. It will be clear, however, that the systems and methodsdescribed herein are to be practiced without some or all of thesespecific details. In other instances, well known process operations havenot been described in detail in order not to unnecessarily obscure thesystems and methods described herein.

As used herein, a system denotes a set of components, real or abstract,comprising a whole where each component interacts with or is related toat least one other component within the whole. Examples of systemsinclude machinery, factories, electrical systems, processing plants,devices, chemical processes, biological systems, data centers, aircraftcarriers, and the like. An electrical system can designate a powergeneration and/or distribution system that is widely dispersed, i.e.,power generation, transformers, and/or electrical distributioncomponents distributed geographically throughout a large region, orbounded within a particular location, e.g., a power plant within aproduction facility, a bounded geographic area, on board a ship, etc.

A network application is any application that is stored on anapplication server connected to a network, e.g., local area network,wide area network, etc., in accordance with any contemporaryclient/server architecture model and can be accessed via the network. Inthis arrangement, the network application programming interface (API)resides on the application server separate from the client machine. Theclient interface would typically be a web browser, e.g. INTERNETEXPLORER™, FIREFOX™, NETSCAPE™, etc., that is in communication with thenetwork application server via a network connection, e.g., HTTP, HTTPS,RSS, etc.

FIG. 1 is an illustration of a system for utilizing real-time data forpredictive analysis of the performance of a monitored system, inaccordance with one embodiment. As shown herein, the system 100 includesa series of sensors, i.e., Sensor A 104, Sensor B 106, Sensor C 108,interfaced with the various components of a monitored system 102, a dataacquisition hub 112, an analytics server 116, and a thin-client device128. In one embodiment, the monitored system 102 is an electrical powergeneration plant. In another embodiment, the monitored system 102 is anelectrical power transmission infrastructure. In still anotherembodiment, the monitored system 102 is an electrical power distributionsystem. In still another embodiment, the monitored system 102 includes acombination of one or more electrical power generation plant(s), powertransmission infrastructure(s), and/or an electrical power distributionsystem. It should be understood that the monitored system 102 can be anycombination of components whose operations can be monitored withconventional sensors and where each component interacts with or isrelated to at least one other component within the combination. For amonitored system 102 that is an electrical power generation,transmission, or distribution system, the sensors can provide data suchas voltage, frequency, current, power, power factor, and the like.

The sensors 104, 106 and 108 can be configured to provide output valuesfor system parameters that indicate the operational status and/or“health” of the monitored system 102. For example, in an electricalpower generation system, the current output or voltage readings for thevarious components that comprise the power generation system isindicative of the overall health and/or operational condition of thesystem. In one embodiment, the sensors are configured to also measureadditional data that can affect system operation. For example, for anelectrical power distribution system, the sensor output can includeenvironmental information, e.g., temperature, humidity, etc., which canimpact electrical power demand and can also affect the operation andefficiency of the power distribution system itself.

Continuing with FIG. 1, in one embodiment, the sensors 104, 106 and 108can be configured to output data in an analog format. For example,electrical power sensor measurements, e.g., voltage, current, etc., aresometimes conveyed in an analog format as the measurements may becontinuous in both time and amplitude. In another embodiment, thesensors 104, 106 and 108 can be configured to output data in a digitalformat. For example, the same electrical power sensor measurements canbe taken in discrete time increments that are not continuous in time oramplitude. In still another embodiment, the sensors 104, 106 and 108 canbe configured to output data in either an analog format, digital format,or both, depending on the sampling requirements of the monitored system102.

The sensors 104, 106 and 108 can be configured to capture output data atsplit-second intervals to effectuate “real time” data capture. Forexample, in one embodiment, the sensors 104, 106 and 108 can beconfigured to generate hundreds of thousands of data readings persecond. It should be appreciated, however, that the number of dataoutput readings taken by a particular sensor can be set to any value aslong as the operational limits of the sensor and the data processingcapabilities of the data acquisition hub 112 are not exceeded.

Still referring to FIG. 1, each sensor 104, 106 and 108 can becommunicatively connected to the data acquisition hub 112 via an analogor digital data connection 110. The data acquisition hub 112 can be astandalone unit or integrated within the analytics server 116 and can beembodied as a piece of hardware, software, or some combination thereof.In one embodiment, the data connection 110 is a “hard wired” physicaldata connection, e.g., serial, network, etc. For example, a serial orparallel cable connection between the sensor and the hub 112. In anotherembodiment, the data connection 110 is a wireless data connection. Forexample, a radio frequency (RF), BLUETOOTH™, infrared or equivalentconnection between the sensor and the hub 112.

The data acquisition hub 112 can be configured to communicate“real-time” data from the monitored system 102 to the analytics server116 using a network connection 114. In one embodiment, the networkconnection 114 is a “hardwired” physical connection. For example, thedata acquisition hub 112 can be communicatively connected, e.g., viaCategory 5 (CAT5), fiber optic, or equivalent cabling, to a data server(not shown) that is communicatively connected, e.g., via CAT5, fiberoptic, or equivalent cabling, through the Internet and to the analyticsserver 116 server. The analytics server 116 can also be communicativelyconnected with the Internet, e.g., via CAT5, fiber optic, or equivalentcabling. In another embodiment, the network connection 114 can be awireless network connection, e.g., Wi-Fi, WLAN, etc. For example,utilizing an 802.11b/g or equivalent transmission format. In practice,the network connection used is dependent upon the particularrequirements of the monitored system 102.

Data acquisition hub 112 can also be configured to supply warning andalarms signals as well as control signals to monitored system 102 and/orsensors 104, 106, and 108 as described in more detail below.

As shown in FIG. 1, in one embodiment, the analytics server 116 can hostan analytics engine 118, virtual system modeling engine 124, and severaldatabases 126, 130, and 132. The virtual system modeling engine 124 can,e.g., be a computer modeling system, such as described above. In thiscontext, however, the modeling engine 124 can be used to precisely modeland mirror the actual electrical system. Analytics engine 118 can beconfigured to generate predicted data for the monitored system andanalyze difference between the predicted data and the real-time datareceived from hub 112.

FIG. 2 is a diagram illustrating a more detailed view of analytic server116. As can be seen, analytic server 116 is interfaced with a monitoredfacility 102 via sensors 202, e.g., sensors 104, 106, and 108. Sensors202 are configured to supply real-time data from within monitoredfacility 102. The real-time data is communicated to analytic server 116via a hub 204. Hub 204 can be configured to provide real-time data toserver 116 as well as alarming, sensing, and control features forfacility 102.

The real-time data from hub 204 can be passed to a comparison engine210, which can form part of analytics engine 118. Comparison engine 210can be configured to continuously compare the real-time data withpredicted values generated by simulation engine 208. Based on thecomparison, comparison engine 210 can be further configured to determinewhether deviations between the real-time and the expected values exists,and if so to classify the deviation, e.g., high, marginal, low, etc. Thedeviation level can then be communicated to decision engine 212, whichcan also comprise part of analytics engine 118.

Decision engine 212 can be configured to look for significant deviationsbetween the predicted values and real-time values as received from thecomparison engine 210. If significant deviations are detected, decisionengine 212 can also be configured to determine whether an alarmcondition exists, activate the alarm and communicate the alarm toHuman-Machine Interface (HMI) 214 for display in real-time via, e.g.,thin client 128. Decision engine 212 can also be configured to performroot cause analysis for significant deviations in order to determine theinterdependencies and identify the parent-child failure relationshipsthat may be occurring. In this manner, parent alarm conditions are notdrowned out by multiple children alarm conditions, allowing theuser/operator to focus on the main problem, at least at first.

Thus, in one embodiment, and alarm condition for the parent can bedisplayed via HMI 214 along with an indication that processes andequipment dependent on the parent process or equipment are also in alarmcondition. This also means that server 116 can maintain a parent-childlogical relationship between processes and equipment comprising facility102. Further, the processes can be classified as critical, essential,non-essential, etc.

Decision engine 212 can also be configured to determine health andperformance levels and indicate these levels for the various processesand equipment via HMI 214. All of which, when combined with the analyticcapabilities of analytics engine 118 allows the operator to minimize therisk of catastrophic equipment failure by predicting future failures andproviding prompt, informative information concerning potential/predictedfailures before they occur. Avoiding catastrophic failures reduces riskand cost, and maximizes facility performance and up time.

Simulation engine 208 operates on complex logical models 206 of facility102. These models are continuously and automatically synchronized withthe actual facility status based on the real-time data provided by hub204. In other words, the models are updated based on current switchstatus, breaker status, e.g., open-closed, equipment on/off status, etc.Thus, the models are automatically updated based on such status, whichallows simulation engine to produce predicted data based on the currentfacility status. This in turn, allows accurate and meaningfulcomparisons of the real-time data to the predicted data.

Example models 206 that can be maintained and used by server 116 includepower flow models used to calculate expected kW, kVAR, power factorvalues, etc., short circuit models used to calculate maximum and minimumavailable fault currents, protection models used to determine properprotection schemes and ensure selective coordination of protectivedevices, power quality models used to determine voltage and currentdistortions at any point in the network, to name just a few. It will beunderstood that different models can be used depending on the systembeing modeled.

In certain embodiments, hub 204 is configured to supply equipmentidentification associated with the real-time data. This identificationcan be cross referenced with identifications provided in the models.

In one embodiment, if the comparison performed by comparison engine 210indicates that the differential between the real-time sensor outputvalue and the expected value exceeds a Defined Difference Tolerance(DDT) value, i.e., the “real-time” output values of the sensor output donot indicate an alarm condition, but below an alarm condition, i.e.,alarm threshold value, a calibration request is generated by theanalytics engine 118. If the differential exceeds the alarm condition,an alarm or notification message can be generated by the analyticsengine 118. If the differential is below the DTT value, the analyticsengine can do nothing and continues to monitor the real-time data andexpected data.

In one embodiment, the alarm or notification message can be sentdirectly to the client or user) 128, e.g., via HMI 214, for display inreal-time on a web browser, pop-up message box, e-mail, or equivalent onthe client 128 display panel. In another embodiment, the alarm ornotification message can be sent to a wireless mobile device, e.g.,BLACKBERRY™, laptop, pager, etc., to be displayed for the user by way ofa wireless router or equivalent device interfaced with the analyticsserver 116. In still another embodiment, the alarm or notificationmessage can be sent to both the client 128 display and the wirelessmobile device. The alarm can be indicative of a need for a repair eventor maintenance to be done on the monitored system. It should be noted,however, that calibration requests should not be allowed if an alarmcondition exists to prevent the models from being calibrated to anabnormal state.

Once the calibration is generated by the analytics engine 118, thevarious operating parameters or conditions of model(s) 206 can beupdated or adjusted to reflect the actual facility configuration. Thiscan include, but is not limited to, modifying the predicted data outputfrom the simulation engine 208, adjusting the logic/processingparameters used by the model(s) 206, adding/subtracting functionalelements from model(s) 206, etc. It should be understood that anyoperational parameter used by models 206 can be modified as long as theresulting modifications can be processed and registered by simulationengine 208.

Referring back to FIG. 1, models 206 can be stored in the virtual systemmodel database 126. As noted, a variety of conventional virtual modelapplications can be used for creating a virtual system model, so that awide variety of systems and system parameters can be modeled. Forexample, in the context of an electrical power distribution system, thevirtual system model can include components for modeling reliability,modeling voltage stability, and modeling power flow. In addition, models206 can include dynamic control logic that permits a user to configurethe models 206 by specifying control algorithms and logic blocks inaddition to combinations and interconnections of generators, governors,relays, breakers, transmission line, and the like. The voltage stabilityparameters can indicate capacity in terms of size, supply, anddistribution, and can indicate availability in terms of remainingcapacity of the presently configured system. The power flow model canspecify voltage, frequency, and power factor, thus representing the“health” of the system.

All of models 206 can be referred to as a virtual system model. Thus, avirtual system model database 130 can be configured to store the virtualsystem model. A duplicate, but synchronized copy of the virtual systemmodel can be stored in a virtual simulation model database 130. Thisduplicate model can be used for what-if simulations. In other words,this model can be used to allow a system designer to make hypotheticalchanges to the facility and test the resulting effect, without takingdown the facility or costly and time consuming analysis. Suchhypothetical can be used to learn failure patterns and signatures aswell as to test proposed modifications, upgrades, additions, etc., forthe facility. The real-time data, as well as trending produced byanalytics engine 118 can be stored in a real-time data acquisitiondatabase 132.

As discussed above, the virtual system model is periodically calibratedand synchronized with “real-time” sensor data outputs so that thevirtual system model provides data output values that are consistentwith the actual “real-time” values received from the sensor outputsignals. Unlike conventional systems that use virtual system modelsprimarily for system design and implementation purposes, i.e., offlinesimulation and facility planning, the virtual system models describedherein are updated and calibrated with the real-time system operationaldata to provide better predictive output values. A divergence betweenthe real-time sensor output values and the predicted output valuesgenerate either an alarm condition for the values in question and/or acalibration request that is sent to the calibration engine 134.

Continuing with FIG. 1, the analytics engine 118 can be configured toimplement pattern/sequence recognition into a real-time decision loopthat, e.g., is enabled by a new type of machine learning calledassociative memory, or hierarchical temporal memory (HTM), which is abiological approach to learning and pattern recognition. Associativememory allows storage, discovery, and retrieval of learned associationsbetween extremely large numbers of attributes in real time. At a basiclevel, an associative memory stores information about how attributes andtheir respective features occur together. The predictive power of theassociative memory technology comes from its ability to interpret andanalyze these co-occurrences and to produce various metrics. Associativememory is built through “experiential” learning in which each newlyobserved state is accumulated in the associative memory as a basis forinterpreting future events. Thus, by observing normal system operationover time, and the normal predicted system operation over time, theassociative memory is able to learn normal patterns as a basis foridentifying non-normal behavior and appropriate responses, and toassociate patterns with particular outcomes, contexts or responses. Theanalytics engine 118 is also better able to understand component meantime to failure rates through observation and system availabilitycharacteristics. This technology in combination with the virtual systemmodel can be characterized as a “neocortical” model of the system undermanagement

This approach also presents a novel way to digest and comprehend alarmsin a manageable and coherent way. The neocortical model could assist inuncovering the patterns and sequencing of alarms to help pinpoint thelocation of the (impending) failure, its context, and even the cause.Typically, responding to the alarms is done manually by experts who havegained familiarity with the system through years of experience. However,at times, the amount of information is so great that an individualcannot respond fast enough or does not have the necessary expertise. An“intelligent” system like the neocortical system that observes andrecommends possible responses could improve the alarm management processby either supporting the existing operator, or even managing the systemautonomously.

Current simulation approaches for maintaining transient stabilityinvolve traditional numerical techniques and typically do not test allpossible scenarios. The problem is further complicated as the numbers ofcomponents and pathways increase. Through the application of theneocortical model, by observing simulations of circuits, and bycomparing them to actual system responses, it may be possible to improvethe simulation process, thereby improving the overall design of futurecircuits.

The virtual system model database 126, as well as databases 130 and 132,can be configured to store one or more virtual system models, virtualsimulation models, and real-time data values, each customized to aparticular system being monitored by the analytics server 118. Thus, theanalytics server 118 can be used to monitor more than one system at atime. As depicted herein, the databases 126, 130, and 132 can be hostedon the analytics server 116 and communicatively interfaced with theanalytics engine 118. In other embodiments, databases 126, 130, and 132can be hosted on a separate database server (not shown) that iscommunicatively connected to the analytics server 116 in a manner thatallows the virtual system modeling engine 124 and analytics engine 118to access the databases as needed.

Therefore, in one embodiment, the client 128 can modify the virtualsystem model stored on the virtual system model database 126 by using avirtual system model development interface using well-known modelingtools that are separate from the other network interfaces. For example,dedicated software applications that run in conjunction with the networkinterface to allow a client 128 to create or modify the virtual systemmodels.

The client 128 can use a variety of network interfaces, e.g., webbrowser, CITRIX™, WINDOWS TERMINAL SERVICES™, telnet, or otherequivalent thin-client terminal applications, etc., to access,configure, and modify the sensors, e.g., configuration files, etc.,analytics engine 118, e.g., configuration files, analytics logic, etc.,calibration parameters, e.g., configuration files, calibrationparameters, etc., virtual system modeling engine 124, e.g.,configuration files, simulation parameters, etc., and virtual systemmodel of the system under management, e.g., virtual system modeloperating parameters and configuration files. Correspondingly, data fromthose various components of the monitored system 102 can be displayed ona client 128 display panel for viewing by a system administrator orequivalent.

As described above, server 116 is configured to synchronize the physicalworld with the virtual and report, e.g., via visual, real-time display,deviations between the two as well as system health, alarm conditions,predicted failures, etc. This is illustrated with the aid of FIG. 3, inwhich the synchronization of the physical world (left side) and virtualworld (right side) is illustrated. In the physical world, sensors 202produce real-time data 302 for the processes 312 and equipment 314 thatmake up facility 102. In the virtual world, simulations 304 of thevirtual system model 206 provide predicted values 306, which arecorrelated and synchronized with the real-time data 302. The real-timedata can then be compared to the predicted values so that differences308 can be detected. The significance of these differences can bedetermined to determine the health status 310 of the system. The healthstats can then be communicated to the processes 312 and equipment 314,e.g., via alarms and indicators, as well as to thin client 128, e.g.,via web pages 316.

FIG. 4 is an illustration of the scalability of a system for utilizingreal-time data for predictive analysis of the performance of a monitoredsystem, in accordance with one embodiment. As depicted herein, ananalytics central server 422 is communicatively connected with analyticsserver A 414, analytics server B 416, and analytics server n 418, i.e.,one or more other analytics servers, by way of one or more networkconnections 114. Each of the analytics servers 414, 416, and 418 iscommunicatively connected with a respective data acquisition hub, i.e.,Hub A 408, Hub B 410, Hub n 412, which communicates with one or moresensors that are interfaced with a system, i.e., Monitored System A 402,Monitored System B 404, Monitored System n 406, which the respectiveanalytical server monitors. For example, analytics server A 414 iscommunicative connected with data acquisition hub A 408, whichcommunicates with one or more sensors interfaced with monitored system A402.

Each analytics server, i.e., analytics server A 414, analytics server B416, analytics server n 418, can be configured to monitor the sensoroutput data of its corresponding monitored system and feed that data tothe central analytics server 422. Additionally, each of the analyticsservers 414, 416 and 418 can function as a proxy agent of the centralanalytics server 422 during the modifying and/or adjusting of theoperating parameters of the system sensors they monitor. For example,analytics server B 416 can be configured as a proxy to modify theoperating parameters of the sensors interfaced with monitored system B404.

Moreover, the central analytics server 422, which is communicativelyconnected to one or more analytics server(s), can be used to enhance thescalability. For example, a central analytics server 422 can be used tomonitor multiple electrical power generation facilities, i.e., monitoredsystem A 402 can be a power generation facility located in city A whilemonitored system B 404 is a power generation facility located in city B,on an electrical power grid. In this example, the number of electricalpower generation facilities that can be monitored by central analyticsserver 422 is limited only by the data processing capacity of thecentral analytics server 422. The central analytics server 422 can beconfigured to enable a client 128 to modify and adjust the operationalparameters of any the analytics servers communicatively connected to thecentral analytics server 422. Furthermore, as discussed above, each ofthe analytics servers 414, 416 and 418 can be configured to serve asproxies for the central analytics server 422 to enable a client 128 tomodify and/or adjust the operating parameters of the sensors interfacedwith the systems that they respectively monitor. For example, the client128 can use the central analytics server 422, and vice versa, to modifyand/or adjust the operating parameters of analytics server A 414 and usethe same to modify and/or adjust the operating parameters of the sensorsinterfaced with monitored system A 402. Additionally, each of theanalytics servers can be configured to allow a client 128 to modify thevirtual system model through a virtual system model developmentinterface using well-known modeling tools.

In one embodiment, the central analytics server 422 can function tomonitor and control a monitored system when its corresponding analyticsserver is out of operation. For example, central analytics server 422can take over the functionality of analytics server B 416 when theserver 416 is out of operation. That is, the central analytics server422 can monitor the data output from monitored system B 404 and modifyand/or adjust the operating parameters of the sensors that areinterfaced with the system 404.

In one embodiment, the network connection 114 is established through awide area network (WAN) such as the Internet. In another embodiment, thenetwork connection is established through a local area network (LAN)such as the company intranet. In a separate embodiment, the networkconnection 114 is a “hardwired” physical connection. For example, thedata acquisition hub 112 can be communicatively connected, e.g., viaCategory 5 (CAT5), fiber optic, or equivalent cabling, to a data serverthat is communicatively connected, e.g., via CAT5, fiber optic, orequivalent cabling, through the Internet and to the analytics server 116server hosting the analytics engine 118. In another embodiment, thenetwork connection 114 is a wireless network connection, e.g., Wi-Fi,WLAN, etc. For example, utilizing an 802.11b/g or equivalenttransmission format.

In certain embodiments, regional analytics servers can be placed betweenlocal analytics servers 414, 416, . . . , 418 and central analyticsserver 422. Further, in certain embodiments a disaster recovery site canbe included at the central analytics server 422 level.

FIG. 5 is a block diagram that shows the configuration details ofanalytics server 116 illustrated in FIG. 1 in more detail. It should beunderstood that the configuration details in FIG. 5 are merely oneembodiment of the items described for FIG. 1, and it should beunderstood that alternate configurations and arrangements of componentscould also provide the functionality described herein.

The analytics server 116 includes a variety of components. In theexample of FIG. 5, the analytics server 116 is implemented in aWeb-based configuration, so that the analytics server 116 includes, orcommunicates with, a secure web server 530 for communication with thesensor systems 519, e.g., data acquisition units, metering devices,sensors, etc., and external communication entities 534, e.g., webbrowser, “thin client” applications, etc. A variety of user views andfunctions 532 are available to the client 128 such as: alarm reports,Active X controls, equipment views, view editor tool, custom userinterface page, and XML parser. It should be appreciated, however, thatthese are just examples of a few in a long list of views and functions532 that the analytics server 116 can deliver to the externalcommunications entities 534 and are not meant to limit the types ofviews and functions 532 available to the analytics server 116 in anyway.

The analytics server 116 also includes an alarm engine 506 and messagingengine 504, for the aforementioned external communications. The alarmengine 506 is configured to work in conjunction with the messagingengine 504 to generate alarm or notification messages 502, in the formof text messages, e-mails, paging, etc., in response to the alarmconditions previously described. The analytics server 116 determinesalarm conditions based on output data it receives from the varioussensor systems 519 through a communications connection, e.g., wireless516, TCP/IP 518, Serial 520, etc., and simulated output data from avirtual system model 512, of the monitored system, processed by theanalytics engines 118. In one embodiment, the virtual system model 512can be created by a user through interacting with an externalcommunication entity 534 by specifying the components that comprise themonitored system and by specifying relationships between the componentsof the monitored system. In another embodiment, the virtual system model512 can be automatically generated by the analytics engines 118 ascomponents of the monitored system are brought online and interfacedwith the analytics server 508.

Continuing with FIG. 5, a virtual system model database 526 can becommunicatively connected with the analytics server 116 and can beconfigured to store one or more virtual system models 512, each of whichrepresents a particular monitored system. For example, the analyticsserver 116 can conceivably monitor multiple electrical power generationsystems, e.g., system A, system B, system C, etc., spread across a widegeographic area, e.g., City A, City B, City C, etc. Therefore, theanalytics server 116 can use a different virtual system model 512 foreach of the electrical power generation systems that it monitors.Virtual simulation model database 538 can be configured to store asynchronized, duplicate copy of the virtual system model 512, andreal-time data acquisition database 540 can store the real-time andtrending data for the system(s) being monitored.

Thus, in operation, analytics server 116 can receive real-time data forvarious sensors, i.e., components, through data acquisition system 202.As can be seen, analytics server 116 can comprise various driversconfigured to interface with the various types of sensors, etc.,comprising data acquisition system 202. This data represents thereal-time operational data for the various components. For example, thedata can indicate that a certain component is operating at a certainvoltage level and drawing certain amount of current. This informationcan then be fed to a modeling engine to generate a virtual system model512 that is based on the actual real-time operational data.

Analytics engine 118 can be configured to compare predicted data basedon the virtual system model 512 with real-time data received from dataacquisition system 202 and to identify any differences. In someinstances, analytics engine can be configured to identify thesedifferences and then update, i.e., calibrate, the virtual system model512 for use in future comparisons. In this manner, more accuratecomparisons and warnings can be generated.

But in other instances, the differences will indicate a failure, or thepotential for a failure. For example, when a component begins to fail,the operating parameters will begin to change. This change may be suddenor it may be a progressive change over time. Analytics engine 118 candetect such changes and issue warnings that can allow the changes to bedetected before a failure occurs. The analytic engine 118 can beconfigured to generate warnings that can be communicated via interface532.

For example, a user can access information from server 116 using thinclient 534. For example, reports can be generate and served to thinclient 534 via server 540. These reports can, for example, compriseschematic or symbolic illustrations of the system being monitored.Status information for each component can be illustrated or communicatedfor each component. This information can be numerical, i.e., the voltageor current level, or it can be symbolic, i.e., green for normal, red forfailure or warning. In certain embodiments, intermediate levels offailure can also be communicated, i.e., yellow can be used to indicateoperational conditions that project the potential for future failure. Itshould be noted that this information can be accessed in real-time.Moreover, via thin client 534, the information can be accessed fromanywhere and anytime.

Continuing with FIG. 5, the Analytics Engine 118 is communicativelyinterfaced with a HTM pattern recognition and machine learning engine551. The HTM engine 551 can be configured to work in conjunction withthe analytics engine 118 and a virtual system model of the monitoredsystem to make real-time predictions, i.e., forecasts, about variousoperational aspects of the monitored system. The HTM engine 551 works byprocessing and storing patterns observed during the normal operation ofthe monitored system over time. These observations are provided in theform of real-time data captured using a multitude of sensors that areimbedded within the monitored system. In one embodiment, the virtualsystem model can also be updated with the real-time data such that thevirtual system model “ages” along with the monitored system. Examples ofa monitored system can include machinery, factories, electrical systems,processing plants, devices, chemical processes, biological systems, datacenters, aircraft carriers, and the like. It should be understood thatthe monitored system can be any combination of components whoseoperations can be monitored with conventional sensors and where eachcomponent interacts with or is related to at least one other componentwithin the combination.

FIG. 6 is a flowchart describing a method for real-time monitoring andpredictive analysis of a monitored system, in accordance with oneembodiment. Method 600 begins with operation 602 where real-time dataindicative of the monitored system status is processed to enable avirtual model of the monitored system under management to be calibratedand synchronized with the real-time data. In one embodiment, themonitored system 102 is a mission critical electrical power system. Inanother embodiment, the monitored system 102 can include an electricalpower transmission infrastructure. In still another embodiment, themonitored system 102 includes a combination of thereof. It should beunderstood that the monitored system 102 can be any combination ofcomponents whose operations can be monitored with conventional sensorsand where each component interacts with or is related to at least oneother component within the combination.

Method 600 moves on to operation 604 where the virtual system model ofthe monitored system under management is updated in response to thereal-time data. This may include, but is not limited to, modifying thesimulated data output from the virtual system model, adjusting thelogic/processing parameters utilized by the virtual system modelingengine to simulate the operation of the monitored system,adding/subtracting functional elements of the virtual system model, etc.It should be understood, that any operational parameter of the virtualsystem modeling engine and/or the virtual system model may be modifiedby the calibration engine as long as the resulting modifications can beprocessed and registered by the virtual system modeling engine.

Method 600 proceeds on to operation 606 where the simulated real-timedata indicative of the monitored system status is compared with acorresponding virtual system model created at the design stage. Thedesign stage models, which may be calibrated and updated based onreal-time monitored data, are used as a basis for the predictedperformance of the system. The real-time monitored data can then providethe actual performance over time. By comparing the real-time time datawith the predicted performance information, difference can be identifieda tracked by, e.g., the analytics engine 118. Analytics engines 118 canthen track trends, determine alarm states, etc., and generate areal-time report of the system status in response to the comparison.

In other words, the analytics can be used to analyze the comparison andreal-time data and determine if there is a problem that should bereported and what level the problem may be, e.g., low priority, highpriority, critical, etc. The analytics can also be used to predictfuture failures and time to failure, etc. In one embodiment, reports canbe displayed on a conventional web browser (e.g. INTERNET EXPLORER™,FIREFOX™, NETSCAPE™, etc., which can be rendered on a standard personalcomputing (PC) device. In another embodiment, the “real-time” report canbe rendered on a “thin-client” computing device, e.g., CITRIX™, WINDOWSTERMINAL SERVICES™, telnet, or other equivalent thin-client terminalapplication. In still another embodiment, the report can be displayed ona wireless mobile device, e.g., BLACKBERRY™, laptop, pager, etc. Forexample, in one embodiment, the “real-time” report can include suchinformation as the differential in a particular power parameter, i.e.,current, voltage, etc., between the real-time measurements and thevirtual output data.

FIG. 7 is a flowchart describing a method for managing real-time updatesto a virtual system model of a monitored system, in accordance with oneembodiment. Method 700 begins with operation 702 where real-time dataoutput from a sensor interfaced with the monitored system is received.The sensor is configured to capture output data at split-secondintervals to effectuate “real time” data capture. For example, in oneembodiment, the sensor is configured to generate hundreds of thousandsof data readings per second. It should be appreciated, however, that thenumber of data output readings taken by the sensor may be set to anyvalue as long as the operational limits of the sensor and the dataprocessing capabilities of the data acquisition hub are not exceeded.

Method 700 moves to operation 704 where the real-time data is processedinto a defined format. This would be a format that can be used by theanalytics server to analyze or compare the data with the simulated dataoutput from the virtual system model. In one embodiment, the data isconverted from an analog signal to a digital signal. In anotherembodiment, the data is converted from a digital signal to an analogsignal. It should be understood, however, that the real-time data may beprocessed into any defined format as long as the analytics engine canutilize the resulting data in a comparison with simulated output datafrom a virtual system model of the monitored system.

Method 700 continues on to operation 706 where the predicted, i.e.,simulated, data for the monitored system is generated using a virtualsystem model of the monitored system. As discussed above, a virtualsystem modeling engine uses dynamic control logic stored in the virtualsystem model to generate the predicted output data. The predicted datais supposed to be representative of data that should actually begenerated and output from the monitored system.

Method 700 proceeds to operation 708 where a determination is made as towhether the difference between the real-time data output and thepredicted system data falls between a set value and an alarm conditionvalue, where if the difference falls between the set value and the alarmcondition value a virtual system model calibration and a response can begenerated. That is, if the comparison indicates that the differentialbetween the “real-time” sensor output value and the corresponding“virtual” model data output value exceeds a Defined Difference Tolerance(DDT) value, i.e., the “real-time” output values of the sensor output donot indicate an alarm condition, but below an alarm condition, i.e.,alarm threshold value, a response can be generated by the analyticsengine. In one embodiment, if the differential exceeds, the alarmcondition, an alarm or notification message is generated by theanalytics engine 118. In another embodiment, if the differential isbelow the DTT value, the analytics engine does nothing and continues tomonitor the “real-time” data and “virtual” data. Generally speaking, thecomparison of the set value and alarm condition is indicative of thefunctionality of one or more components of the monitored system.

FIG. 8 is a flowchart describing a method for synchronizing real-timesystem data with a virtual system model of a monitored system, inaccordance with one embodiment. Method 800 begins with operation 802where a virtual system model calibration request is received. A virtualmodel calibration request can be generated by an analytics enginewhenever the difference between the real-time data output and thepredicted system data falls between a set value and an alarm conditionvalue.

Method 800 proceeds to operation 804 where the predicted system outputvalue for the virtual system model is updated with a real-time outputvalue for the monitored system. For example, if sensors interfaced withthe monitored system outputs a real-time current value of A, then thepredicted system output value for the virtual system model is adjustedto reflect a predicted current value of A.

Method 800 moves on to operation 806 where a difference between thereal-time sensor value measurement from a sensor integrated with themonitored system and a predicted sensor value for the sensor isdetermined. As discussed above, the analytics engine is configured toreceive “real-time” data from sensors interfaced with the monitoredsystem via the data acquisition hub, or, alternatively directly from thesensors, and “virtual” data from the virtual system modeling enginesimulating the data output from a virtual system model of the monitoredsystem. In one embodiment, the values are in units of electrical poweroutput, i.e., current or voltage, from an electrical power generation ortransmission system. It should be appreciated, however, that the valuescan essentially be any unit type as long as the sensors can beconfigured to output data in those units or the analytics engine canconvert the output data received from the sensors into the desired unittype before performing the comparison.

Method 800 continues on to operation 808 where the operating parametersof the virtual system model are adjusted to minimize the difference.This means that the logic parameters of the virtual system model that avirtual system modeling engine uses to simulate the data output fromactual sensors interfaced with the monitored system are adjusted so thatthe difference between the real-time data output and the simulated dataoutput is minimized. Correspondingly, this operation will update andadjust any virtual system model output parameters that are functions ofthe virtual system model sensor values. For example, in a powerdistribution environment, output parameters of power load or demandfactor might be a function of multiple sensor data values. The operatingparameters of the virtual system model that mimic the operation of thesensor will be adjusted to reflect the real-time data received fromthose sensors. In one embodiment, authorization from a systemadministrator is requested prior to the operating parameters of thevirtual system model being adjusted. This is to ensure that the systemadministrator is aware of the changes that are being made to the virtualsystem model. In one embodiment, after the completion of all the variouscalibration operations, a report is generated to provide a summary ofall the adjustments that have been made to the virtual system model.

As described above, virtual system modeling engine 124 can be configuredto model various aspects of the system to produce predicted values forthe operation of various components within monitored system 102. Thesepredicted values can be compared to actual values being received viadata acquisition hub 112. If the differences are greater than a certainthreshold, e.g., the DTT, but not in an alarm condition, then acalibration instruction can be generated. The calibration instructioncan cause a calibration engine 134 to update the virtual model beingused by system modeling engine 124 to reflect the new operatinginformation.

It will be understood that as monitored system 102 ages, or morespecifically the components comprising monitored system 102 age, thenthe operating parameters, e.g., currents and voltages associated withthose components will also change. Thus, the process of calibrating thevirtual model based on the actual operating information provides amechanism by which the virtual model can be aged along with themonitored system 102 so that the comparisons being generated byanalytics engine 118 are more meaningful.

At a high level, this process can be illustrated with the aid of FIG. 9,which is a flow chart illustrating an example method for updating thevirtual model in accordance with one embodiment. In step 902, data iscollected from, e.g., sensors 104, 106, and 108. For example, thesensors can be configured to monitor protective devices within anelectrical distribution system to determine and monitor the ability ofthe protective devices to withstand faults, which is describe in moredetail below.

In step 904, the data from the various sensors can be processed byanalytics engine 118 in order to evaluate various parameters related tomonitored system 102. In step 905, simulation engine 124 can beconfigured to generate predicted values for monitored system 102 using avirtual model of the system that can be compared to the parametersgenerated by analytics engine 118 in step 904. If there are differencesbetween the actual values and the predicted values, then the virtualmodel can be updated to ensure that the virtual model ages with theactual system 102.

It should be noted that as the monitored system 102 ages, variouscomponents can be repaired, replaced, or upgraded, which can also createdifferences between the simulated and actual data that is not an alarmcondition. Such activity can also lead to calibrations of the virtualmodel to ensure that the virtual model produces relevant predictedvalues. Thus, not only can the virtual model be updated to reflect agingof monitored system 102, but it can also be updated to reflectretrofits, repairs, etc.

As noted above, in certain embodiments, a logical model of a facilitieselectrical system, a data acquisition system (data acquisition hub 112),and power system simulation engines (modeling engine 124) can beintegrated with a logic and methods based approach to the adjustment ofkey database parameters within a virtual model of the electrical systemto evaluate the ability of protective devices within the electricaldistribution system to withstand faults and also effectively “age” thevirtual system with the actual system.

Only through such a process can predictions on the withstand abilitiesof protective devices, and the status, security and health of anelectrical system be accurately calculated. Accuracy is important as thepredictions can be used to arrive at actionable, mission critical orbusiness critical conclusions that may lead to the re-alignment of theelectrical distribution system for optimized performance or security.

FIGS. 10-12 are flow charts presenting logical flows for determining theability of protective devices within an electrical distribution systemto withstand faults and also effectively “age” the virtual system withthe actual system in accordance with one embodiment. FIG. 10 is adiagram illustrating an example process for monitoring the status ofprotective devices in a monitored system 102 and updating a virtualmodel based on monitored data. First, in step 1002, the status of theprotective devices can be monitored in real time. As mentioned,protective devices can include fuses, switches, relays, and circuitbreakers. Accordingly, the status of the fuses/switches, relays, and/orcircuit breakers, e.g., the open/close status, source and load status,and on or off status, can be monitored in step 1002. It can bedetermined, in step 1004, if there is any change in the status of themonitored devices. If there is a change, then in step 1006, the virtualmodel can be updated to reflect the status change, i.e., thecorresponding virtual components data can be updated to reflect theactual status of the various protective devices.

In step 1008, predicted values for the various components of monitoredsystem 102 can be generated. But it should be noted that these valuesare based on the current, real-time status of the monitored system. Instep 1010, it can be determined which predicted voltages are for avalue, such as a value for a node or load, which can be calibrated. Atthe same time, real time sensor data can be received in step 1012. Thisreal time data can be used to monitor the status in step 1002 and it canalso be compared with the predicted values in step 1014. As noted above,the difference between the predicted values and the real time data canalso be determined in step 1014.

Accordingly, meaningful predicted values based on the actual conditionof monitored system 102 can be generated in steps 1004 to 1010. Thesepredicted values can then be used to determine if further action shouldbe taken based on the comparison of step 1014. For example, if it isdetermined in step 1016 that the difference between the predicted valuesand the real time sensor data is less than or equal to a certainthreshold, e.g., DTT, then no action can be taken e.g., an instructionnot to perform calibration can be issued in step 1018. Alternatively, ifit is determined in step 1020 that the real time data is actuallyindicative of an alarm situation, e.g., is above an alarm threshold,then a do not calibrate instruction can be generated in step 1018 and analarm can be generated as described above. If the real time sensor datais not indicative of an alarm condition, and the difference between thereal time sensor data and the predicted values is greater than thethreshold, as determined in step 1022, then an initiate calibrationcommand can be generated in step 1024.

If an initiate calibration command is issued in step 1024, then afunction call to calibration engine 134 can be generated in step 1026.The function call will cause calibration engine 134 to update thevirtual model in step 1028 based on the real time sensor data. Acomparison between the real time data and predicted data can then begenerated in step 1030 and the differences between the two computed. Instep 1032, a user can be prompted as to whether or not the virtual modelshould in fact be updated. In other embodiments, the update can beautomatic, and step 1032 can be skipped. In step 1034, the virtual modelcould be updated. For example, the virtual model loads, buses, demandfactor, and/or percent running information can be updated based on theinformation obtained in step 1030. An initiate simulation instructioncan then be generated in step 1036, which can cause new predicted valuesto be generated based on the update of virtual model.

In this manner, the predicted values generated in step 1008 are not onlyupdated to reflect the actual operational status of monitored system102, but they are also updated to reflect natural changes in monitoredsystem 102 such as aging. Accordingly, realistic predicted values can begenerated in step 1008.

FIG. 11 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored instep 1002. Depending on the embodiment, the protective devices can beevaluated in terms of the International Electrotechnical Commission(IEC) standards or in accordance with the United States or AmericanNational Standards Institute (ANSI) standards. It will be understood,that the process described in relation to FIG. 11 is not dependent on aparticular standard being used.

First, in step 1102, a short circuit analysis can be performed for theprotective device. Again, the protective device can be any one of avariety of protective device types. For example, the protective devicecan be a fuse or a switch, or some type of circuit breaker. It will beunderstood that there are various types of circuit breakers includingLow Voltage Circuit Breakers (LVCBs), High Voltage Circuit Breakers(HVCBs), Mid Voltage Circuit Breakers (MVCBs), Miniature CircuitBreakers (MCBs), Molded Case Circuit Breakers (MCCBs), Vacuum CircuitBreakers, and Air Circuit Breakers, to name just a few. Any one of thesevarious types of protective devices can be monitored and evaluated usingthe processes illustrated with respect to FIGS. 10-12.

For example, for LVCBs, or MCCBs, the short circuit current, symmetric(I_(sym)) or asymmetric (I_(asym)), and/or the peak current (I_(peak))can be determined in step 1102. For, e.g., LVCBs that are notinstantaneous trip circuit breakers, the short circuit current at adelayed time (I_(symdelay)) can be determined. For HVCBs, a first cycleshort circuit current (I_(sym)) and/or I_(peak) can be determined instep 1102. For fuses or switches, the short circuit current, symmetricor asymmetric, can be determined in step 1102. And for MVCBs the shortcircuit current interrupting time can be calculated. These are just someexamples of the types of short circuit analysis that can be performed inStep 1102 depending on the type of protective device being analyzed.

Once the short circuit analysis is performed in step 1102, various stepscan be carried out in order to determine the bracing capability of theprotective device. For example, if the protective device is a fuse orswitch, then the steps on the left hand side of FIG. 11 can be carriedout. In this case, the fuse rating can first be determined in step 1104.In this case, the fuse rating can be the current rating for the fuse.For certain fuses, the X/R can be calculated in step 1105 and theasymmetric short circuit current (I_(asym)) for the fuse can bedetermined in step 1106 using equation 1.

I _(ASYM) =I _(SYM)√{square root over (1+2e ^(−2p/(X/R)))}  Eq 1

In other implementations, the inductants/reactants (X/R) ratio can becalculated in step 1108 and compared to a fuse test X/R to determine ifthe calculated X/R is greater than the fuse test X/R. The calculated X/Rcan be determined using the predicted values provided in step 1008.Various standard tests X/R values can be used for the fuse test X/Rvalues in step 1108. For example, standard test X/R values for a LVCBcan be as follows:

PCB,ICCB=6.59

MCCB,ICCB rated<=10,000 A=1.73

MCCB,ICCB rated 10,001-20,000 A=3.18

MCCB,ICCB rated>20,000 A=4.9

If the calculated X/R is greater than the fuse test X/R, then in step1112, equation 12 can be used to calculate an adjusted symmetrical shortcircuit current (I_(adjsym)).

$\begin{matrix}{I_{ADJSYM} = {I_{SYM}\begin{Bmatrix}\sqrt{1 + {2\; ^{{- 2}{p/{({{CALC}\mspace{14mu} {X/R}})}}}}} \\\sqrt{1 + {2\; ^{{- 2}{p/{({{TEST}\mspace{11mu} {X/R}})}}}}}\end{Bmatrix}}} & {{Eq}\mspace{14mu} 12}\end{matrix}$

If the calculated X/R is not greater than the fuse test X/R thenI_(adjsym) can be set equal to I_(sym) in step 1110. In step 1114, itcan then be determined if the fuse rating (step 1104) is greater than orequal to I_(adjsym) or I_(asym). If it is, then it can determine in step1118 that the protected device has passed and the percent rating can becalculated in step 1120 as follows:

${\% \mspace{14mu} {rating}} = \frac{I_{ADJSYM}}{Devicerating}$ or${\% \mspace{14mu} {rating}} = \frac{I_{ASYM}}{Devicerating}$

If it is determined in step 1114 that the device rating is not greaterthan or equal to I_(adjsym), then it can be determined that the deviceas failed in step 1116. The percent rating can still be calculating instep 1120.

For LVCBs, it can first be determined whether they are fused in step1122. If it is determined that the LVCB is not fused, then in step 1124can be determined if the LVCB is an instantaneous trip LVCB. If it isdetermined that the LVCB is an instantaneous trip LVCB, then in step1130 the first cycle fault X/R can be calculated and compared to acircuit breaker test X/R (see example values above) to determine if thefault X/R is greater than the circuit breaker test X/R. If the fault X/Ris not greater than the circuit breaker test X/R, then in step 1132 itcan be determined if the LVCB is peak rated. If it is peak rated, thenI_(peak) can be used in step 1146 below. If it is determined that theLVCB is not peak rated in step 1132, then I_(adjsym) can be set equal toI_(sym) in step 1140. In step 1146, it can be determined if the devicerating is greater or equal to I_(adjsym), or to I_(peak) as appropriate,for the LVCB.

If it is determined that the device rating is greater than or equal toI_(adjsym), then it can be determined that the LVCB has passed in step1148. The percent rating can then be determined using the equations forI_(adjsym) defined above (step 1120) in step 1152. If it is determinedthat the device rating is not greater than or equal to I_(adjsym), thenit can be determined that the device has failed in step 1150. Thepercent rating can still be calculated in step 1152.

If the calculated fault X/R is greater than the circuit breaker test X/Ras determined in step 1130, then it can be determined if the LVCB ispeak rated in step 1134. If the LVCB is not peak rated, then theI_(adjsym) can be determined using equation 12. If the LVCB is not peakrated, then I_(peak) can be determined using equation 11.

I _(PEAK)=√{square root over (2)}I _(SYM){1.02+0.98e ^(−3/(X/R))}  Eq 11

It can then be determined if the device rating is greater than or equalto I_(adjsym) or I_(peak) as appropriate. The pass/fail determinationscan then be made in steps 1148 and 1150 respectively, and the percentrating can be calculated in step 1152.

${\% \mspace{14mu} {rating}} = \frac{I_{ADJSYM}}{Devicerating}$ or${\% \mspace{14mu} {rating}} = \frac{I_{PEAK}}{Devicerating}$

If the LVCB is not an instantaneous trip LVCB as determined in step1124, then a time delay calculation can be performed at step 1128followed by calculation of the fault X/R and a determination of whetherthe fault X/R is greater than the circuit breaker test X/R. If it isnot, then I_(adjsym) can be set equal to I_(sym) in step 1136. If thecalculated fault at X/R is greater than the circuit breaker test X/R,then I_(adjsymdelay) can be calculated in step 1138 using the followingequation with, e.g., a 0.5 second maximum delay:

$\begin{matrix}{I_{ADJSYMDELAY} = {I_{SYMDELAY}\begin{Bmatrix}\sqrt{1 + {2\; ^{{- 60}\; {p/{({{CALC}\mspace{14mu} {X/R}})}}}}} \\\sqrt{1 + {2\; ^{{- 60}{p/{({{TEST}\mspace{11mu} {X/R}})}}}}}\end{Bmatrix}}} & {{Eq}.\mspace{14mu} 14}\end{matrix}$

It can then be determined if the device rating is greater than or equalto I_(adjsym) or I_(adjsymdelay). The pass/fail determinations can thenbe made in steps 1148 and 1150, respectively and the percent rating canbe calculated in step 1152.

If it is determined that the LVCB is fused in step 1122, then the faultX/R can be calculated in step 1126 and compared to the circuit breakertest X/R in order to determine if the calculated fault X/R is greaterthan the circuit breaker test Xs/R. If it is greater, then I_(adjsym)can be calculated in step 1154 using the following equation:

$\begin{matrix}{I_{ADJSYM} = {I_{SYM}\begin{Bmatrix}\sqrt{1.02 + {0.98\; ^{{- 3}/{({{CALC}\mspace{14mu} {X/R}})}}}} \\\sqrt{1.02 + {0.98\; ^{{- 3}/{({{TEST}\mspace{14mu} {X/R}})}}}}\end{Bmatrix}}} & {{Eq}.\mspace{14mu} 13}\end{matrix}$

If the calculated fault X/R is not greater than the circuit breaker testX/R, then I_(adjsym) can be set equal to I_(sym) in step 1156. It canthen be determined if the device rating is greater than or equal toI_(adjsym) in step 1146. The pass/fail determinations can then becarried out in steps 1148 and 1150 respectively, and the percent ratingcan be determined in step 1152.

FIG. 12 is a diagram illustrating an example process for determining theprotective capabilities of a HVCB. In certain embodiments, the X/R canbe calculated in step 1157 and a peak current (I_(peak)) can bedetermined using equation 11 in step 1158. In step 1162, it can bedetermined whether the HVCB's rating is greater than or equal toI_(peak) as determined in step 1158. If the device rating is greaterthan or equal to I_(peak), then the device has passed in step 1164.Otherwise, the device fails in step 1166. In either case, the percentrating can be determined in step 1168 using the following:

${\% \mspace{14mu} {rating}} = \frac{I_{PEAK}}{Devicerating}$

In other embodiments, an interrupting time calculation can be made instep 1170. In such embodiments, a fault X/R can be calculated and thencan be determined if the fault X/R is greater than or equal to a circuitbreaker test X/R in step 1172. For example, the following circuitbreaker test X/R can be used;

50 Hz Test X/R=13.7

60 Hz Test X/R=16.7

(DC Time contant=0.45 ms)

If the fault X/R is not greater than the circuit breaker test X/R thenI_(adjintsym) can be set equal to I_(sym) in step 1174. If thecalculated fault X/R is greater than the circuit breaker test X/R, thencontact parting time for the circuit breaker can be determined in step1176 and equation 15 can then be used to determine I_(adjintsym) in step1178.

$\begin{matrix}{I_{ADJINTSYM} = {I_{INTSYM}\begin{Bmatrix}\sqrt{1 + {2\; ^{{- 4}{pf}*{t/{({{CALC}\mspace{14mu} {X/R}})}}}}} \\\sqrt{1 + {2\; ^{{- 4}p*{t/{({{TEST}\mspace{14mu} {X/R}})}}}}}\end{Bmatrix}}} & {{Eq}.\mspace{14mu} 15}\end{matrix}$

In step 1180, it can be determined whether the device rating is greaterthan or equal to I_(adjintsym). The pass/fail determinations can then bemade in steps 1182 and 1184 respectively and the percent rating can becalculated in step 1186 using the following:

${\% \mspace{14mu} {rating}} = \frac{I_{ADJINTSYM}}{Devicerating}$

FIG. 13 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored instep 1002 in accordance with another embodiment. The process can startwith a short circuit analysis in step 1302. For systems operating at afrequency other than 60 hz, the protective device X/R can be modified asfollows:

(X/R)mod=(X/R)*60 H/(system Hz).

For fuses/switches, a selection can be made, as appropriate, between useof the symmetrical rating or asymmetrical rating for the device. TheMultiplying Factor (MF) for the device can then be calculated in step1304. The MF can then be used to determine I_(adjasym) or I_(adjsym). Instep 1306, it can be determined if the device rating is greater than orequal to I_(adjasym) or I_(adjsym). Based on this determination, it canbe determined whether the device passed or failed in steps 1308 and 1310respectively, and the percent rating can be determined in step 1312using the following:

% rating=I _(adjasym)*100/device rating; or

% rating=I _(adjsym)*100/device rating.

For LVCBs, it can first be determined whether the device is fused instep 1314. If the device is not fused, then in step 1315 it can bedetermined whether the X/R is known for the device. If it is known, thenthe LVF can be calculated for the device in step 1320. It should benoted that the LVF can vary depending on whether the LVCB is aninstantaneous trip device or not. If the X/R is not known, then it canbe determined in step 1317, e.g., using the following:

-   -   The X/R is equal to:

PCB,ICCB=6.59

MCCB,ICCB rated<=10,000 A=1.73

MCCB,ICCB rated 10,001-20,000 A=3.18

MCCB,ICCB rated>20,000 A=4.9

If the device is fused, then in step 1316 it can again be determinedwhether the X/R is known. If it is known, then the LVF can be calculatedin step 1319. If it is not known, then the X/R can be set equal to,e.g., 4.9.

In step 1321, it can be determined if the LVF is less than 1 and if itis, then the LVF can be set equal to 1. In step 1322 I_(intadj) can bedetermined using the following:

MCCB/ICCB/PCB With Instantaneous:

lint,adj=LVF*Isym,rms

PCB Without Instantaneous:

lint,adj=LVFp*Isym,rms(½Cyc)

int,adj=LVFasym*Isym,rms(3-8Cyc)

In step 1323, it can be determined whether the device's symmetricalrating is greater than or equal to I_(intadj), and it can be determinedbased on this evaluation whether the device passed or failed in steps1324 and 1325 respectively. The percent rating can then be determined instep 1326 using the following:

% rating=I _(intadj)*100/device rating.

FIG. 14 is a diagram illustrating a process for evaluating the withstandcapabilities of a MVCB in accordance with one embodiment. In step 1328,a determination can be made as to whether the following calculationswill be based on all remote inputs, all local inputs or on a No AC Decay(NACD) ratio. For certain implementations, a calculation can then bemade of the total remote contribution, total local contribution, totalcontribution (I_(intrmssym)), and NACD. If the calculated NACD is equalto zero, then it can be determined that all contributions are local. IfNACD is equal to 1, then it can be determined that all contributions areremote.

If all the contributions are remote, then in step 1332 the remote MF(MFr) can be calculated and I_(int) can be calculated using thefollowing:

I _(int)=MFr*I _(intrmssym).

If all the inputs are local, then MF1 can be calculated and I_(int) canbe calculated using the following:

I _(int)=MF1*I _(intrmssym).

If the contributions are from NACD, then the NACD, MFr, MF1, and AMF1can be calculated. If AMF1 is less than 1, then AMF1 can be set equalto 1. I_(int) can then be calculated using the following:

I _(int)=AMF1*I _(intrmssym) /S.

In step 1338, the 3-phase device duty cycle can be calculated and thenit can be determined in step 1340, whether the device rating is greaterthan or equal to I_(int). Whether the device passed or failed can thenbe determined in steps 1342 and 1344, respectively. The percent ratingcan be determined in step 1346 using the following:

% rating=I _(int)*100/3p device rating.

In other embodiments, it can be determined, in step 1348, whether theuser has selected a fixed MF. If so, then in certain embodiments thepeak duty (crest) can be determined in step 1349 and MFp can be setequal to 2.7 in step 1354. If a fixed MF has not been selected, then thepeak duty (crest) can be calculated in step 1350 and MFp can becalculated in step 1358. In step 1362, the MFp can be used to calculatethe following:

I _(mompeak)=MFp*I _(symrms).

In step 1366, it can be determined if the device peak rating (crest) isgreater than or equal to I_(mompeak). It can then be determined whetherthe device passed or failed in steps 1368 and 1370 respectively, and thepercent rating can be calculated as follows:

% rating=I _(mompeak)*100/device peak(crest)rating.

In other embodiments, if a fixed MF is selected, then a momentary dutycycle (C&L) can be determined in step 1351 and MFm can be set equal to,e.g., 1.6. If a fixed MF has not been selected, then in step 1352 MFmcan be calculated. MFm can then be used to determine the following:

I _(momsym)=MFm*I _(symrms).

It can then be determined in step 1374 whether the device C&L, rmsrating is greater than or equal to I_(momsym). Whether the device passedor failed can then be determined in steps 1376 and 1378 respectively,and the percent rating can be calculated as follows:

% rating=I _(momasym)*100/device C&L,rms rating.

Thus, the above methods provide a mean to determine the withstandcapability of various protective devices, under various conditions andusing various standards, using an aged, up to date virtual model of thesystem being monitored.

The influx of massive sensory data, e.g., provided via sensors 104, 106,and 108, intelligent filtration of this dense stream of data intomanageable and easily understandable knowledge. For example, asmentioned, it is important to be able to assess the real-time ability ofthe power system to provide sufficient generation to satisfy the systemload requirements and to move the generated energy through the system tothe load points. Conventional systems do not make use of an on-line,real-time system snap shot captured by a real-time data acquisitionplatform to perform real time system availability evaluation.

It should also be noted that National Fire Protection Association (NFPA)and the Occupational Safety and Health Association (OSHA) have mandatedthat facilities comply with proper workplace safety standards andconduct Arc Flash studies in order to determine the incident energy,protection boundaries and personal protective equipment (PPE) levelsrequired to be worn by technicians. Unfortunately, conventionalapproaches for performing such studies do not provide a reliable meansfor the real-time prediction of the potential energy released (incalories per centimeter squared) for an Arc Flash event, protectionboundaries, or the PPE level required to safely perform repairs asrequired by NFPA 70E and Institute of Electrical and Electrics Engineers(IEEE) 1584.

When a fault in the system being monitored contains an arc, the heatreleased can damage equipment and cause personal injury. It is thelatter concern that brought about the development of the heat exposureprograms, i.e., NFPA 70E, IEEE 1584, referred to above. The powerdissipated in the arc radiates to the surrounding surfaces. The furtheraway from the arc the surface is, the less the energy is received perunit area.

As noted previously, conventional approaches are based on highlyspecialized static simulation models that are rigid and non-reflectiveof the facility's operational status at the time that a technician maybe needed to conduct repairs on the electrical equipment. For example,static systems cannot adjust to the many daily changes to the electricalsystem that occur at a facility, e.g., motors and pumps may be on oroff, on-site generation status may have changed by having dieselgenerators on-line, utility electrical feed may also change, etc., norcan they age with the facility. That is, the incident energy released isaffected by the actual operational status of the facility and alignmentof the power distribution system at the time that the repairs areperformed. Therefore, a static model cannot provide the real-timeanalysis that can be critical for accurate safe protection boundary orPPE level determination.

Moreover, existing systems rely on exhaustive studies to be performedoff-line by a power system engineer or a design professional/specialist.Often the specialist must manually modify a simulation model so that itis reflective of the proposed facility operating condition and thenconduct a static simulation or a series of static simulations in orderto come up with incident energy estimates for determining safe workingdistances and required PPE levels. Such a process is not timely,efficient, and/or accurate. Plus, the process can be quite costly.

Using the systems and methods described herein, a logical model of afacility electrical system can be integrated into a real-timeenvironment with a robust arc flash simulation engine, a dataacquisition system (data acquisition hub), and an automatic feedbacksystem (analytics engine) that continuously synchronizes and calibratesthe logical model to the actual operational conditions of the electricalsystem. The ability to re-align the logical model in real-time so thatit mirrors the real facility operating conditions, coupled with theability to calibrate and age the model as the real facility ages, asdescribe above, provides a desirable approach to predicting PPE levels,and safe working conditions at the exact time the repairs are intendedto be performed. Accordingly, facility management can provide real-timecompliance with NFPA 70E and IEEE 1584 standards and requirements.

FIG. 15 is a diagram illustrating how an Arch Flash simulation engineworks in conjunction with the other elements of the analytics system tomake predictions about various aspects of an Arc Flash event on anelectrical system, in accordance with one embodiment. As depictedherein, the arc flash simulation engine 1502 is housed within ananalytics server 116 and communicatively connected via a networkconnection 114 with a data acquisition hub 112, a client terminal 128and a virtual system model database 526. The virtual system modeldatabase 526 is configured to store a virtual system model of theelectrical system 102. The virtual system model is constantly updatedwith real-time data from the data acquisition hub 112 to effectivelyaccount for the natural aging effects of the hardware that comprise thetotal electrical system 102, thus, mirroring the real operatingconditions of the system.

The arc flash simulation engine 1502 can be configured to process systemdata from real-time data fed from the hub 112 and predicted data outputfrom a real-time virtual system model of the electrical system 102 tomake predictions about various aspects of an Arc Flash event that occurson the electrical system 102. It should be appreciated that the arcflash simulation engine 1502 is further configured to make predictionsabout both alternating current (AC) and direct current (DC) Arc Flashevents.

The data acquisition hub 112 is communicatively connected via dataconnections 110 to a plurality of sensors that are embedded throughoutthe electrical system 102. The data acquisition hub 112 may be astandalone unit or integrated within the analytics server 116 and can beembodied as a piece of hardware, software, or some combination thereof.In one embodiment, the data connections 110 are “hard wired” physicaldata connections, e.g., serial, network, etc. For example, a serial orparallel cable connection between the sensors and the hub 112. Inanother embodiment, the data connections 110 are wireless dataconnections. For example, a radio frequency (RF), BLUETOOTH™, infraredor equivalent connection between the sensor and the hub 112.

Continuing with FIG. 15, the client 128 is typically a conventional“thin-client” or “thick client” computing device that may utilize avariety of network interfaces, e.g., web browser, CITRIX™, WINDOWSTERMINAL SERVICES™, telnet, or other equivalent thin-client terminalapplications, etc., to access, configure, and modify the sensors, e.g.,configuration files, etc., analytics engine, e.g., configuration files,analytics logic, etc., calibration parameters, e.g., configurationfiles, calibration parameters, etc., Arc Flash Simulation Engine, e.g.,configuration files, simulation parameters, etc., and virtual systemmodel of the electrical system 102 under management, e.g., virtualsystem model operating parameters and configuration files.Correspondingly, in one embodiment, the data from the various componentsof the electrical system 102 and the real-time predictions (forecasts)about the various aspects of an Arc Flash event on the system can becommunicated on a client 128 display panel for viewing by a systemadministrator or equivalent. For example, the aspects may becommunicated by way of graphics, i.e., charts, icons, etc., or textdisplayed on the client 128 display panel. In another embodiment, theaspects can be communicated by way of synthesized speech or soundsgenerated by the client 128 terminal. In still another embodiment, theaspects can be summarized and communicated on a hard copy report 1502generated by a printing device interfaced with the client 128 terminal.In yet still another embodiment, the aspects can be communicated by wayof labels generated by a printing device interfaced with the client 128terminal. It should be understood, however, that there are a myriad ofdifferent methods available to communicate the aspects to a user andthat the methods listed above are provided here by way of example only.

As discussed above, the arc flash simulation engine 1502 can beconfigured to work in conjunction with a real-time updated virtualsystem model of the electrical system 102 to make predictions(forecasts) about certain aspects of an AC or DC Arc Flash event thatoccurs on the electrical system 102. For example, in one embodiment, theArc Flash Simulation Engine 1502 can be used to make predictions aboutthe incident energy released on the electrical system 102 during the ArcFlash event. Examples of protective devices include but are not limitedto switches, molded case circuits (MCCs), circuit breakers, fuses,relays, etc.

In order to calculate the incident energy released during an Arc Flashevent, data must be collected about the facility's electrical system102. This data is provided by a virtual system model of the electricalsystem 102 stored on the virtual system model database 526communicatively linked to the arc flash simulation engine 1502. Asdiscussed above, the virtual system model is continuously updated withreal-time data provided by a plurality of sensors interfaced to theelectrical system 102 and communicatively linked to the data acquisitionhub 112. In one embodiment, this data includes the arrangement ofcomponents on a one-line drawing with nameplate specifications for everydevice comprising the electrical system. Also included are details ofthe lengths and cross section area of all cables. Once the data has beencollected, a short circuit analysis followed by a coordination study isperformed by the Arc Flash Simulation Engine 1502 (NOTE: Since the NFPA70E and IEEE 1584 standards do not directly apply to DC arc faults, a DCfault short circuit study is performed during simulations of DC ArcFlash events instead of the standard 3-phase fault short circuit studyfor AC Arc Flash events). The resultant data is then fed into theequations supplied by the NFPA 70E standard, IEEE Standard 1584, orequivalent standard. These equations will calculate the incident energyreleased by the Arc Flash event to, e.g., determine the necessary flashprotection boundary distances and minimum PPE level requirements.

For example, in one embodiment the PPE level relates to a level requiredpersonal protective equipment (PPE) for personnel operating within theconfines of the system during the Arc Flash event. For example, Table Ais a NFPA 70E tabular summary of the required PPE level, i.e., PPECategory, for each given quantity of incident energy released by the ArcFlash event.

TABLE A Category Cal/cm² Clothing 0 1.2 Untreated Cotton 1 4 Flameretardant (FR) shirt and FR pants 2 8 Cotton underwear FR shirt and FRpants 3 25 Cotton underwear FR shirt, FR pants and FR coveralls 4 40Cotton underwear FR shirt, FR pants and double layer switching coat andpants

In still another embodiment, the PPE level relates to a minimum ArcFlash protection boundary around protective devices on the electricalsystem 102 during an Arc Flash event. That is, the minimum distancepersonnel must maintain away from protective devices that are subject toArc Flash events. These minimum protection boundaries can becommunicated via printed on labels that are affixed to the protectivedevices as a warning for personnel working in the vicinity of thedevices.

FIG. 16 is a diagram illustrating an example process for predicting, inreal-time, various aspects associated with an AC or DC Arc Flashincident, in accordance with one embodiment. These aspects can includefor example, the Arc Flash incident energy, Arc Flash protectionboundary, and required Personal Protective Equipment (PPE) levels, incompliance with NFPA-70E and IEEE-1584 standards, for personnel workingin the vicinity of protective devices that are susceptible to Arc Flashevents. First, in step 1602, updated virtual system model data can beobtained for the system being simulated, e.g., the updated data of step1006, and the operating modes for the various components that comprisethe system can be determined. This includes data that will later be usedin system short circuit and/or protective device studies and systemschematic diagrams in the form of one-line drawings. Examples of thetypes of data that are provided by the virtual system model for a DCanalysis are summarized below in Table B. Examples of the types of datathat are provided by the virtual system model for an AC analysis aresummarized below in Table C. It should be appreciated that the datasummarized in Tables B and C are provided herein by example only and isnot intended to limit the types of data stored by and extracted from thevirtual system model.

TABLE B Short Circuit Study Data System Diagrams Protective Device StudyGenerator data One-line drawings Low Voltage Breaker trip Motor dataSystem blueprints settings Reactor data Fuse type and size Breaker dataFuse data Cable data Battery data

TABLE C Short Circuit Study Data System Diagrams Protective Device StudyCable/Transmission One-line drawings Low Voltage Breaker trip line dataSystem blueprints settings Motor data Transformer data Fuse type andsize Utility data CT Ratios Generator data Relay Types/Settings Reactordata Breaker data Fuse data

In step 1604, a short circuit analysis, 3-phase fault for AC arc faultsimulations and 1-phase fault for DC Arc Flash simulations, can beperformed in order to obtain bolted fault current values for the system.The short-circuit study is based on a review of one-line drawingprovided by the virtual system model of the system. Maximum availablebolted fault current is calculated for each point in the system that issusceptible to an Arc Flash event. Typically, the Arc Flash vulnerablepoints are the protective devices that are integrated to the electricalsystem. In step 1606, the bolted fault current values are communicatedto the Arc Flash simulation engine that is configured to makepredictions about certain aspects associated with the Arc Flash eventsthat occur on the system.

In step 1608, Arc Flash bus data for certain components, i.e.,protective devices, on the electrical system are communicated to the ArcFlash simulation engine. Examples of the types of equipment data sentduring this step include, but are not limited to: switchgear data, MCCdata, panel data, cable data, etc. In step 1610, a standardized method,i.e., NFPA 70E, IEEE 1584, etc., is chosen for the Arc Flash simulationand incident energy calculation. For example, in one embodiment, asystem administrator may configure the Arc Flash simulation engine touse either the NFPA 70E or IEEE 1584 standards to simulate the Arc Flashevent and determine the quantity of incident energy released by the ArcFlash event. In another embodiment, the Arc Flash simulation engine isconfigured to simulate the Arc Flash event and calculate incident energyusing both standards, taking the larger of the resultant incident energynumbers for use in making various predictions about aspects associatedwith the Arc Flash event. That is, the predicted aspects will always bebased upon the most conservative estimates of the Arc Flash incidentenergy released.

If the IEEE 1584 method is chosen to simulate the Arc Flash event andcalculate the incident energy, then the Arc Flash simulation engineperforms, in step 1612, a protective device study on a specificprotective device, such as a circuit breaker or fuse on the system. Thisstudy determines the operational settings of that protective device andsends that information to the Arc Flash engine for use in the subsequentArc Flash event simulation and incident energy calculations. In step1614, the Arc Flash engine calculates two different arcing currentvalues, a 100% arcing current value and an 85% arcing current value, forthe system using the bolted fault current value supplied by the shortcircuit study and the system voltage value supplied by the virtualsystem model simulation. This is to account for fluctuations in systemvoltage values that normally occur during the day to day operation ofthe electrical system. To account for the fluctuations two arcingcurrent and incident energy calculations are made; one using thecalculated expected arc current, i.e., 100% arcing current, and oneusing a reduced arc current that is 15% lower, i.e., 85% arcing current,to account for when the system operates at less than 1 kilovolts (kV).In step 1616, the fault clearing times in the protective device can bedetermined using the arcing currents values and protective devicesettings determined in steps 1612 and 1614.

In step 1618, the IEEE 1584 equations can be applied to the faultclearing time (determined in step 1616) and the arcing current values,both the 100% and 85% arcing current values, to predict the incidentenergy released by an Arc Flash event occurring on the protective deviceduring a 100% arc current scenario, i.e., expected arc current level,and an 85% arc current scenario, i.e., reduced arc current level. The100% and 85% arcing current incident energy values are then comparedagainst each other with the higher of the two being selected for use indetermining certain aspects associated with the Arc Flash event. Forexample, in one embodiment, the aspect relates to the required PPElevels for personnel. In another embodiment, the aspect relates to theArc Flash protection boundary around the protective device.

If the NFPA 70E method is chosen to simulate the Arc Flash event, theArc Flash simulation engine proceeds directly to step 1620 where theincident arcing energy level is calculated by applying the boltedcurrent values determined in step 1604, the fault clearing timedetermined in step 1616, and the system voltage values to equationssupplied by NFPA 70E standard. The calculated incident arc energy levelvalue is then used by the Arc Flash simulation engine to makepredictions about certain aspects of the Arc Flash event. For example,in one embodiment, the incident arc energy level is referenced againstTable 130.7(C)(9)(a) of NFPA 70E to predict the required PPE levels forpersonnel operating around the protective device experiencing the ArcFlash event being simulated. In another embodiment, the safe workingboundary distance is determined using the equation supplied by paragraph130.3(A) of the NFPA.

It should be noted that the NFPA 70E steps may only apply to ACcalculations. As noted above, there are no equations/standards for DCcalculations. Accordingly, in certain embodiments, DC determinations aremade using the IEEE 1584 equations and substituting the single phaseshot circuit analysis in step 1604. In certain embodiments, a similarsubstitution can be made for NFPA 70E DC determinations.

In step 1622, Arc Flash labels and repair work orders based upon theabove discussed predictions may be generated by the Arc Flash simulationengine. That is appropriate protective measures, clothing and procedurescan be mobilized to minimize the potential for injury should an ArcFlash incident occur. Thus allowing facility owners and operators toefficiently implement a real-time safety management system that is incompliance with NFPA 70E and IEEE 1584 guidelines.

In step 1624, the aspects are communicated to the user. In oneembodiment, the aspects are communicated by way of graphics, i.e.,charts, icons, etc., or text displayed on a client display panel. Inanother embodiment, the aspects are communicated by way of synthesizedspeech or sound generated by the client terminal. In still anotherembodiment, the aspects are summarized and communicated on a hard copyreport generated by a printing device interfaced with the clientterminal. In yet still another embodiment, the aspects are communicatedby way of labels generated by a printing device interfaced with theclient terminal. It should be understood, however, that there are amyriad of different methods available to communicate the aspects to auser and that the methods listed above are provided here by way ofexample only.

Using the same or a similar procedure as illustrated in FIG. 16, thefollowing AC evaluations can be made in real-time and based on anaccurate, e.g., aged, model of the system:

-   -   Arc Flash Exposure based on IEEE 1584;    -   Arc Flash Exposure based on NFPA 70E;    -   Network-Based Arc Flash Exposure on AC Systems/Single Branch        Case;    -   Network-Based Arc Flash Exposure on AC Systems/Multiple Branch        Cases;    -   Network Arc Flash Exposure on DC Networks;    -   Exposure Simulation at Switchgear Box, MCC Box, Open Area and        Cable Grounded and Ungrounded;    -   Calculate and Select Controlling Branch(s) for Simulation of Arc        Flash;    -   Test Selected Clothing;    -   Calculate Clothing Required;    -   Calculate Safe Zone with Regard to User Defined Clothing        Category;    -   Simulated Art Heat Exposure at User Selected locations;    -   User Defined Fault Cycle for 3-Phase and Controlling Branches;    -   User Defined Distance for Subject;    -   100% and 85% Arcing Current;    -   100% and 85% Protective Device Time;    -   Protective Device Setting Impact on Arc Exposure Energy;    -   User Defined Label Sizes;    -   Attach Labels to One-Line Diagram for User Review;    -   Plot Energy for Each Bus;    -   Write Results into Excel;    -   View and Print Graphic Label for User Selected Bus(s); and    -   Required work permits.

Using the same or a similar procedure as illustrated in FIG. 16, thefollowing DC evaluations can be made in real-time and based on anaccurate, e.g., aged, model of the system:

-   -   DC Arc Flash Exposure    -   Network-Based Arc Flash Exposure on DC Systems/Single Branch        Case    -   Network-Based Arc Flash Exposure on DC Systems/Multiple Branch        Cases    -   Exposure Simulation at Switchgear Box, MCC Box, Open Area and        Cable Grounded and Ungrounded    -   Calculate and Select Controlling Branch(s) for Simulation of DC        Arc Flash    -   Test Selected Clothing    -   Calculate Clothing Required    -   Calculate Safe Zone with Regard to User Defined Clothing        Category    -   Simulated DC Art Heat Exposure at User Selected locations    -   User Defined Fault Cycle for DC and Controlling Branches    -   User Defined Distance for Subject    -   100% and 85% Arcing Current    -   100% and 85% Protective Device Time    -   Protective Device Setting Impact on DC Arc Exposure Energy    -   User Defined Label Sizes    -   Attach Labels to Equipment/Interface/Diagram for User Review    -   Plot Energy for Each Bus    -   Write Results into Excel    -   View and Print Graphic Label for User Selected Bus(s)    -   Required work permit

As noted, Arc Flash is the analysis of the potentially life threateningexplosive discharge of energy in a radiated arc away from the source.Often, determination of the highest potential arcing energy should bebased on the actual values in the past thirty days. Conventionalstandards and practices do not guarantee that highest energy will bedetected; because all the possible scenarios are not, or could not be,evaluated or because the on-line changes in the system make the previousanalysis obsolete.

No reliable means exists for the real time determination of thepotential for Arc Flash energy that is consistent with the standardsestablished in both NFPA-70E, IEEE-1584 procedures for accuratelypredicting the potential for Arc Flash by automatically including theexisting potential energy based on the highest and lowest short circuitcurrent in the previous 30 days. Conventional calculation of Arc Flashenergy and corresponding PPE as defined in both NFPA-70E, IEEE-1584 isbased on calculation of both the arcing current and tripping time. Ingeneral, higher arcing current corresponds to higher energy; however,due to the nonlinear relationship between the arcing current andtripping time, it is possible that low arcing current can cause hightripping time and yield the higher Arc Flash energy. Therefore,conventional Arc Flash determinations are performed off-line and arebased on the evaluation of several scenarios and calculation of thehighest and lowest short-circuit, and arcing current, and then selectingthe scenario that yields the highest Arcing Energy. The shortcomings ofsuch conventional processes are the required subjective choice ofscenarios by the engineer performing the analysis. Also, in the case ofhigh-order systems it is not feasible to access all the scenarios.

FIG. 17 is a flow chart illustrating an example process for an exampleprocess for predicting, in real-time, various aspects associated with anAC or DC Arc Flash incident using a virtual no load scenario as a secondcritical input, in accordance with one embodiment. In the processillustrated in FIG. 17, the results are accurate and truly reflect thecorrect potential energy. Consequently, such a process automaticallypresents the worst case scenario produced in a real time. Further, thesystem operator is provided the ability to perform the same balancedanalysis based on hypothetical conditions to determine the probableresults and impact prior to making any physical change to theenvironment providing “what if” planning.

The process of FIG. 17 builds on the data acquired and analyzed inreal-time as described above. A virtual no load scenario is thengenerated as the second critical input into the analysis for determiningtrue Arc Flash or Arc Heat potential, providing a real-time analysisthat complies with NFPA-70E and IEEE-1584. Thus, process illustrated inFIG. 17 provides an automated method for determining the greatestpotential Arc Flash energy based on empirical measured values and doesnot rely on the subjective analysis or incorrect assumptions of typicalstatic or off line systems

First, in step 1702, the virtual system model is updated to reflectcurrent network topology, e.g., breakers ON/OFF, Generators ON/OFF,UPS's in by-pass, etc., as indicated by the real-time data. In step1702, all the observable motors in the virtual model, e.g., chillers andother devices for which current can be monitored and for which statedoes not depend on the upstream circuit breaker, can be switched ON orOFF. In step 1706, all the UPS's equipped with a transfer switch in theby-pass position are transferred. These devices will change state in theevent of arc flash. In step 1708, all unobservable motors are set to theON position in the virtual model. This will cause the maximum shortcircuit and arcing current.

In step 1710, the arcing energy for all significant locations in thesystem are then calculated. In step 1712, all unobservable motors arethen switched to the OFF position in the virtual model. This will causethe minimum short circuit and arcing current. The arcing energy for allsignificant locations in the system is then calculated again in step1714. In step 1716, the two calculated values of steps 1710 and 1714 arecompared and the highest value for each significant location in thesystem can be stored.

In step 1718, all the UPS's can be transferred back to the correctposition and the process can be repeated after a predetermined delay.The delay can be set based on the requirements of a particularimplementation.

Now, in step 1720, the worst case Arc Flash for the last 30 days can beaccessed on demand. In one embodiment, the worst case Arc Flash can becommunicated by way of graphics, i.e., charts, icons, etc, or textdisplayed on a client display panel. In another embodiment, the worstcase Arc Flash can be communicated by way of synthesized speech or soundgenerated by the client terminal. In still another embodiment, the worstcase Arc Flash can be summarized and communicated on a hard copy reportgenerated by a printing device interfaced with the client terminal. Inyet still another embodiment, the worst case Arc Flash can becommunicated by way of labels generated by a printing device interfacedwith the client terminal. It should be understood, however, that thereare a myriad of different methods available to communicate the aspectsto a user and that the methods listed above are provided here by way ofexample only.

The embodiments described herein, can be practiced with other computersystem configurations including hand-held devices, microprocessorsystems, microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers and the like. The embodiments canalso be practiced in distributing computing environments where tasks areperformed by remote processing devices that are linked through anetwork.

It should also be understood that the embodiments described herein canemploy various computer-implemented operations involving data stored incomputer systems. These operations are those requiring physicalmanipulation of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. Further, the manipulations performed are often referred toin terms, such as producing, identifying, determining, or comparing.

Any of the operations that form part of the embodiments described hereinare useful machine operations. The invention also relates to a device oran apparatus for performing these operations. The systems and methodsdescribed herein can be specially constructed for the required purposes,such as the carrier network discussed above, or it may be a generalpurpose computer selectively activated or configured by a computerprogram stored in the computer. In particular, various general purposemachines may be used with computer programs written in accordance withthe teachings herein, or it may be more convenient to construct a morespecialized apparatus to perform the required operations.

The embodiments described herein can also be embodied as computerreadable code on a computer readable medium. The computer readablemedium is any data storage device that can store data, which canthereafter be read by a computer system. Examples of the computerreadable medium include hard drives, network attached storage (NAS),read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetictapes, and other optical and non-optical data storage devices. Thecomputer readable medium can also be distributed over a network coupledcomputer systems so that the computer readable code is stored andexecuted in a distributed fashion.

Certain embodiments can also be embodied as computer readable code on acomputer readable medium. The computer readable medium is any datastorage device that can store data, which can thereafter be read by acomputer system. Examples of the computer readable medium include harddrives, network attached storage (NAS), read-only memory, random-accessmemory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical andnon-optical data storage devices. The computer readable medium can alsobe distributed over a network coupled computer systems so that thecomputer readable code is stored and executed in a distributed fashion.

Although a few embodiments of the present invention have been describedin detail herein, it should be understood, by those of ordinary skill,that the present invention may be embodied in many other specific formswithout departing from the spirit or scope of the invention. Therefore,the present examples and embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details provided therein, but may be modified and practicedwithin the scope of the appended claims.

What is claimed:
 1. A system for making real-time predictions about anarc flash event on an electrical system, comprising: a data acquisitioncomponent communicatively connected to a sensor configured to acquirereal-time data output from the electrical system; an analytics servercommunicatively connected to the data acquisition component, comprising:a virtual system modeling engine configured to generate predicted dataoutput for the electrical system using a virtual system model of theelectrical system, an analytics engine configured to monitor thereal-time data output and the predicted data output of the electricalsystem, and an arc flash simulation engine configured to use the virtualsystem model updated based in the real-time data to forecast an aspectof the arc flash event.